Data Engineer

• Safeguard the reliability, performance, and usability of end-to-end digital solutions
• Embed automated testing, AI-assisted workflows, and modern test tooling into our delivery lifecycle
• Define test strategy, build automation frameworks, and validate quality across modern web applications and mobile apps

Senior Data Engineer, Spark, Databricks

• Design, develop, and maintain data pipelines and ETL processes using Microsoft Azure services (e.g., Azure Data Factory, Azure Synapse, Azure Databricks, Azure Fabric).
• Utilize Azure data storage accounts for organizing and maintaining data pipeline outputs. (e.g., Azure Data Lake Storage Gen 2 & Azure Blob storage).
• Collaborate with data scientists, data analysts, data architects and other stakeholders to understand data requirements and deliver high-quality data solutions.
• Optimize data pipelines in the Azure environment for performance, scalability, and reliability.
• Ensure data quality and integrity through data validation techniques and frameworks.
• Develop and maintain documentation for data processes, configurations, and best practices.
• Monitor and troubleshoot data pipeline issues to ensure timely resolution.
• Stay current with industry trends and emerging technologies to ensure our data solutions remain cutting-edge.
• Manage the CI/CD process for deploying and maintaining data solutions.

Azure Data Engineer

• Design, build, and optimize the data infrastructure that powers reporting, analytics, and business intelligence capabilities
• Manage data synchronization across disparate database paradigms and enable stakeholders through modern BI tools
• Safeguard core production databases from intensive analytical workloads and unauthorized data extraction
• Write clean, maintainable, and typed code within the existing software ecosystem
• Develop backend services, scripts, and internal tools leveraging TypeScript and the MERN stack

• Build and optimize real-time streaming pipelines to ensure efficient routing of voice and chat session data into enterprise data platforms
• Integrate generative AI solutions with enterprise knowledge bases to ensure accurate and context-aware conversational agent performance
• Maintain and configure low-latency databases to ensure secure and reliable storage of session states and operational metadata
• Design system architectures and technical blueprints to ensure scalable, resilient, and high-performance solutions
• Delegate and manage engineering tasks to ensure effective team productivity and capability development
• Develop and execute testing processes to identify system issues and ensure quality and stability in deployed solutions
• Collaborate with stakeholders to align requirements, manage expectations, and ensure successful project delivery
• Oversee automated PII redaction workflows across voice and text streams to ensure full compliance with data privacy requirements prior to storage.

Senior Data Engineering Developer

• Transform, validate, and migrate critical data while ensuring accuracy, integrity, and a smooth transition.
• Support data conversion, validation, cleansing, and migration activities for client implementations.
• Ensure the accuracy, integrity, and successful transfer of data from legacy systems into new platforms.
• Provide exceptional support throughout the implementation process.
• Work with large datasets and collaborate with clients to ensure seamless transitions during system implementations.

Data Migration Specialist

• Design, develop, and maintain scalable ETL/ELT pipelines using Azure Data Factory, Azure Databricks, and other relevant services
• Develop and maintain the company's data architecture, including data models, schemas, and data dictionaries.
• Integrate data from various sources, ensuring data quality and consistency, and prepare it for analytical and operational use
• Follow consistency of data governance policies and processes to ensure data security, privacy, and compliance.
• Collaborate with other data professionals, including data scientists, analysts, and business team members, to understand data requirements and develop solutions to meet those needs.
• Stay up to date with the latest tools and technologies and see opportunities to enhance Manulife's data infrastructure.
• Ability to learn and implement best practices of big data and cloud computing to ensure solutions are scalable, efficient, and effective.

• Design, develop, and maintain scalable ETL/ELT pipelines using Azure Data Factory and Azure Databricks
• Collaborate with data professionals to understand data requirements and develop solutions
• Ensure data quality and consistency from various sources
• Follow data governance policies to ensure data security and compliance
• Stay up to date with tools and technologies for enhancing data infrastructure

Senior Data Engineer

• Design, build, and maintain scalable and secure cloud-native data platforms and data pipelines.
• Lead the architecture, optimisation, and operational management of the Snowflake data warehouse platform.
• Develop robust ELT/ETL pipelines to ingest, transform, and deliver high-quality data from multiple internal and external sources.
• Build reusable and maintainable data frameworks, transformation models, and orchestration workflows.
• Develop and maintain infrastructure-as-code and automation for data platform provisioning and management where appropriate.
• Optimise performance, scalability, and cost efficiency across data storage, transformation, and query workloads.
• Support near real-time and batch-based data processing requirements.
• Own and continuously improve Snowflake architecture, performance tuning, security, governance, and operational best practices.
• Design and optimise Snowflake schemas, warehouses, clustering strategies, and data sharing capabilities.
• Monitor Snowflake usage, query performance, and cost consumption to drive optimisation initiatives.
• Support data lifecycle management, retention, and governance policies within Snowflake.
• Enable self-service analytics capabilities through well-structured semantic layers and governed datasets.
• Collaborate closely with Product, Engineering, Operations, Finance, and Business stakeholders to deliver impactful data solutions.

• Design, build, and maintain scalable ETL/ELT pipelines using Databricks (Apache Spark)
• Implement batch and streaming data ingestion from multiple sources (databases, APIs, event streams)
• Ensure pipelines are fault-tolerant, efficient, and cost-optimized on AWS
• Develop and maintain data lake / lakehouse architectures using AWS S3, Delta Lake, and Databricks
• Implement medallion architecture (Bronze / Silver / Gold layers)
• Optimize data storage formats (Parquet, Delta) and partitioning strategies
• Work with AWS services such as S3 (data lake storage), IAM (access control), Lambda / Step Functions (orchestration)
• Develop notebooks and jobs using PySpark / SQL / Python
• Optimize Spark jobs for performance and cost (cluster sizing, caching, joins, shuffles)
• Manage Databricks Workflows, jobs, and cluster configurations
• Implement Unity Catalog for governance and data access control (if used)

• Build Silver layer dbt models for each dataset phase, staging models, intermediate models, and conformed fact and dimension tables
• Define and implement incremental load strategies for each entity, upsert logic, snapshot tables, and data freshness handling
• Write and maintain dbt schema tests, not-null, uniqueness, referential integrity, accepted values, and custom generic tests
• Collaborate with the analytics engineer on the Silver-to-Gold interface, ensuring Silver models expose the fields and grain needed for Gold metric calculations
• Work with IT during each phase, confirming entity availability, field mappings, and Bronze table quality before build commences
• In Run steady state: own pipeline incident response, schema drift handling, and Snowflake performance monitoring across all live datasets
• Investigate and resolve pipeline incidents escalated by the AI triage layer.
• Manage upstream schema changes affecting Bronze-to-Silver mappings.
• Monitor and optimize Snowflake query performance across live Gold datasets.
• Own and maintain the AI triage pipeline, ensuring alert classification accuracy and incident log completeness.
• Ensure escalated alerts are acknowledged and resolved within agreed SLA timelines.
• Maintain accurate data catalogue documentation and quality certifications across production Gold datasets.

• Design and deliver modern data platforms using tools like Snowflake, Microsoft Fabric, Azure, Fivetran, Coalesce, dbt, and Power BI
• Build and maintain scalable data pipelines and data models
• Ensure data quality, reliability, and governance standards are met
• Support data integration, transformation, and warehousing processes
• Contribute to internal standards, frameworks, and best practices
• Collaborate with technical teams to improve data architecture and performance
• Continuously improve data workflows and engineering processes

• Implement complex databases and data pipelines that put analytics at the heart of real-time decision-making.
• Work closely with leaders across the company on high-profile analytics projects that drive business strategies.
• Provide technical guidance and ensure the use of sound engineering practices and effective use of resources.
• Advance our implementation capabilities and help our team deliver solutions faster.
• Work with strategic partners to solve business problems by developing best-in-class data solutions.
• Perform data manipulation using SQL and Python to develop data assets.
• Leverage technology to automate data ingestion, transformation, and integration.
• Evaluate data quality and completeness.
• Build strong partnerships with peers across the organization to support data-related goals.
• Explore new technologies and data sources with curiosity and creativity.
• Identify, design, and implement internal process improvements: automating manual processes and optimizing data pipelines.
• Discover and explore new sources of data with curiosity and creativity.
• Create and maintain optimal data pipeline architecture.

Data Engineer – Mid

• Work closely with predictive modelers to assemble large, complex data sets that help solve business problems and increase profitability
• Perform data manipulation and transformation using SQL and Python
• Identify, design, and implement internal process improvements: automating manual processes and optimizing data pipelines
• Discover and explore new sources of data with curiosity and creativity
• Create and maintain optimal data pipeline architecture
• Author detailed and comprehensible documentation of data pipelines and technical solutions
• Ensure data quality and completeness

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